Use normalization and ETL to get the big data results you want

Extract, transform, load (ETL) is the most talked-about strategy for extracting data from multiple sources and systems, and then recombining it into new datasets for query purposes. ETL has been used in enterprises to populate data marts and warehouses for years. But ETL won’t be enough to link data into close associations so it can be normalized in order to eliminate some of the extraneous “data fishing” that is likely to occur when analytic queries go after multiple forms of the same data in an attempt to assimilate that data to produce answers. For this, you need data normalization.